Journal of Environmentally Friendly Processes: Volume 2. Issue 3. June 2014
Petrotex Library Archive
Journal of Environmentally Friendly Processes
Journal Website: http://www.petrotex.us/2013/03/26/586/
Conceptual model for solar power plant site selection by fuzzy logic model
and ordered weighted averaging method (A case study Yazd province-IRAN)
Nasehi, S1, Sakineh Shadkam Birak Olia, S1, Karimi, S1,* , Jafari, H.R1
1
Faculty of Environment, University of Tehran, Tehran, Iran
Abstract:
Unique features of solar energy have caused increasing demands for such resources in various countries. In order to get this source of
energy, power plants have been established. The more the establishments are, the more we need a suitable location to construct the
power plant. The objective of this paper is choose the best model for location solar power plant.in this research Solar power plant site
selection based on the fuzzy logic and ordered weighted averaging (OWA). Which OWA able to involve priority layers through their
weight, and Multiple Criteria Decision Making (MCDM) proses. Using the Analytic Network Process (ANP) we determined the
weight of each criterion. In this way after identification, valuation of criteria layers by using fuzzy method because of their uncertainty
and determined their importance, the layers combined. Final map was divided into four classes: suitable, moderately suitable,
relatively unsuitable and unsuitable. The results showed that application of fuzzy logic, OWA, MCDM and ANP model have a high
accuracy in locating solar power plant. In the study area, a lot of regions can be determined to be high suitability. In order to acquire
the highest value to establish a solar power plant, the final map was divided into 10 categories. Five sites were introduced the candidate
sites. These sites reduce the power plant investment costs due to locating in a suitable place.
Keyword: Solar power plant, Fuzzy model, ANP model, Yazd province, OWA method
1.
Introduction
Sustainable development and prosperity of a society would require energy as a key element [1]. Energy can be as the driving engine
for economic development all over the world. The sustainable energy is defined as combining the equal allocation of energy to all
people and subsequent generations’ environmental protection [2]. Global energy resources can be classified into the following three
groups. Fossil energies (oil, gas, coal, etc.), Nuclear energy, and renewable energies (wind, solar, geothermal, hydro-power, biomass,
hydrogen, ocean, etc.) [3]. Since fossil fuels generate most of the world's energy supply, fossil fuel use has an important impact on
the world economy, ecology, and global climate [4]. Using renewable energy resources is raised in the 21st century because the fossil
fuels resources are reducing and also the environment is contaminating century [5]. Comparing to the other kinds of energy, solar
power generation has some benefits such as environmental advantages, government incentives, flexible locations and modularity
[4].Renewable energy is really important because it provides some advantages and would cause contamination and environmental
damages. Among all the Renewable Energy resources, solar energy seems to lead the Iran’s economic sector towards sustainability
in future. RES usually vary temporarily and spatially. They would eradicate the troubles caused by fossil and nuclear energies since
the state enjoys an abundance of solar radiation [6]. Renewable energy is one of the best options. It would increase the energy
efficiency by decreasing the negative impacts of climate change [7]. At least one local source of renewable energy can generally be
found at almost any location on the Earth's surface. While Sun provides 99.8% of the energy of the Earth's surface, solar energy is
one of the most inexpensive, pollution-free, inexhaustible renewable energy resources [8]. Selecting a site for solar power system is
difficult and has puzzled the electricity generation enterprises, grid enterprises and government. Since determining the sites for power
plant determines the plant's security, it should fulfill the meteorology requirement, economics requirement, environment and society
requirement [9]. Locating between 25 and 40 north latitude, Iran is in a favorable situation in terms of the received amount of solar
energy. Solar radiation in Iran is estimated to be about 1800–2200kWh/m2 per year, which is more eminent than normal. Over more
than 90% of Iran's territorial land has an average of more than 280 sunny days, which is a highly significant potential source of energy
Authors /Journal of Environmentally Friendly Processes 3 (2014) 45-47
[3].Multi criteria decision analysis’s Recent developments offer two classes of decision rules that can be considered as specific cases
of a family of OWA [10]. OWA has been developed as a generalization of multi criteria combination [11]. Integrating the OWA
concept has been a core of GIS multi criteria decision analysis over the last decade or so. Eastman has extended the GIS applications
by OWA - as a part of a decision support module in GIS-IDRISI. In subsequent years, Jiang and Eastman found that the GIS-OWA
would be useful for land use/suitability problems [12]. Various applications of OWA to environmental and urban planning problems
show that the OWA concept could be executed in IDRISI15.01 [13]. The important view of integrating the GIS and OWA capabilities
is the manner of the order weights. For instance, the IDRISI-OWA procedure does not supply a user (decision maker) with a method
vfor getting the order weights [12]. These complex problems require simultaneous evaluation of many measures. For this purpose,
MCDM can assist decision makers in choosing the best alternative [14].
This method is a process that consists of ascertaining the best alternative among a set of viable options. The purpose or ultimate goal
of an MCDM method is to investigate a number of alternatives in the light of criteria and conflicting objectives [15].In order to
overcome this shortcoming, Saaty proposed the ANP , which is a generalization of the AHP . While the AHP represents a framework
with unidirectional hierarchical relationships, the ANP allows for complex interrelationships among decision levels and attributes
[16]. In other words, ANP represents a decision problem as a mesh of components grouped into clusters [17]. The components of a
cluster may influence some or all the elements of any other cluster, which signifies that a network may include the interdependence
of cluster and/or feedback within them [18]. In short, ANP allows for working with interdependent criteria and offers a more accurate
approach for modelling complex environments [19]. Fuzzy set theory is an extension of the classical set theory, which is based on
two valued logic; that is, in or out. In other words, membership is dichotomous: an element is either a member or not. Fuzzy sets, on
the other hand, were formulated by Zadeh and based on the simple idea of bringing out a degree of membership of an element [20].
The central concept of fuzzy sets, which has relevance and intuitive meaning to the sustainability assessment process, is the
‘membership function.
The number of studies aiming at an evaluation of locational suitability, or the optimization of sites for solar power plant development,
and which additionally apply appropriate MCDM methods is low and we review some of them, here. Carrión et al. [21], in a work
by using Environmental decision-support systems, optimal site selection for grid-connected photovoltaic power plants. They to
provide layer Land Proportionality grid-connected photovoltaic power plants use of The AHP.
Beccali et al. [22] used the ELECTRE III method to evaluate a plan of action for the dissemination of renewable energy technologies
at regional level. Izquierdo et al. [23] and Partners, in a study use of Analytic Hierarchy Process and Vector layers, offered a method
for estimating the geographical distribution of the available roof surface area for large-scale photovoltaic energy-potential evaluations
in urban areas. They apply this methodology, mean available area for photovoltaic equipment on roofs of 14 ±4/5 m^2/ca. With a
confidence level of 95% is obtained. Sen [24] proposed a nonlinear model to estimate the total solar radiation using the sunshine
hour’s data available. Hofierka & Kaňuk, [25] in a study present a methodology for the assessment of photovoltaic potential in urban
areas using open-source solar radiation tools and a 3D city model implemented in a geographic information system. Provide this solar
radiation tools by R. Sun solar radiation model and using the PVGIS Software estimation utility. Dagdougui et al. [26] proposed a
GIS-based decision making methodology for the selection of the most promising locations for installing renewable hydrogen
production systems. Based on the combination of a Geographic Information System (GIS) and tools or multi-criteria decision making
(MCDM) methods in order to obtain the evaluation of the optimal placement of photovoltaic solar power plants in southeast Spain.
Aragonés-Beltrán et al. [27] in a study titled "An ANP-based approach for the selection of photovoltaic solar power plant investment
projects published the results of their studies. They chose by using Analytic Network Process of the four variables, the best variable
based on risk minimization for photovoltaic solar power plant projects.
Djurdjevic [28], carried out a study with purpose of is to review some key issues and prospects related to solar photovoltaic power
engineering in the Republic of Serbia. Presented in this study solar radiation maps using PVGIS software. The results of this study
show that the Republic of Serbia has great potential for utilizing standalone and grid connected solar PV energy systems.
Gastli and Charabi [29], in study predicted the solar energy potential for power generation in Oman using GIS maps. In their study,
they first reviewed the methods developed for creating solar radiation maps using GIS tools and then developed Oman's solar radiation
GIS maps for the months of January and July. They also used a number of methods to calculate the annual electrical energy generation
potential. The results showed that the country had the potential to use solar energy all year long.
Zoghi et al. [30], used Fuzzy logic model and Weighted Linear Combination method for optimizing the solar site selection in Iran arid and semi-arid region. The results showed that the combination of fuzzy logic, WLC and MCDM, and also positioning in locating
optimal solar sites have a high accuracy.
This paper is focused on integrating ANP with GIS in order for selecting the most appropriate solar site power plant in Yazd province,
Iran Through the combination of multi criteria decision making processes with fuzzy logic and ordered weighted averaging(OWA).
Therefore, the major goal of this paper is to choose the best models for location solar power plant.
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Authors /Journal of Environmentally Friendly Processes 3 (2014) 45-47
2. Materials and Methods
2.1. Case study
Global radiation reaching the earth’s surface is much stronger in arid or semi-arid zones than in tropical or equatorial humid zones.
Iran is a developing country located in the Middle East with around 1, 648,195 km2 and about 73 million populations. Average annual
rainfall in Iran is about 288 mm; average temperature in summer and in winter 19–38 °C and10–25 °C respectively. The average solar
radiation in Iran is about 19.23MJ/m2. Solar energy in the north and south has been recorded around 2.8 kwh/m2 and 5.4 kwh/m2
respectively [31]. Yazd province is located in the central part of Iran. Since this province locates in the sunny-belt, solar radiation in
this region is relatively good. Yazd is located at 31° 90N and 54° 37E, and is 1237, 2 m above the sea level and covers an area about
131,575km2 [32]. Solar radiation on the horizontal surface of Yazd city, as the capital of Yazd province, has been recorded as 7787
MJ/ m2. Figure 1 shows the location of the study area in solar energy map.
Figure1. Introduction of the study area in a term of solar energy zoning
2.2. Method
At first, the literature and methods of locating a solar power plant provided by other researchers were surveyed. Then, in order to
determine the best location for a solar power plant in Yazd, a province in Iran, multi-criteria evaluation method of analytical network
was selected based on the research objectives. This model was selected among other models due to the fact that it is feasible to quantify
qualitative indicators in this model.
The process of weighting the criteria was taken place by network analysis. Analytical Network Process (ANP) takes into account the
complex relationship between and among decision elements and this feature of ANP provides a precise attitude towards the issues.
In this study, weighting and prioritizing criteria were carried out by the views of planning and management of the environment and
renewable energies experts. In this study, ten relevant criteria were used. These criteria were categorized into four groups including
environmental (distance from fault, and land cover), climatic (Humidity, Solar radiation), geomorphologic (Aspect, elevation,
geology, slope), and location (Distance from urban Solar radiation). Also, four layers (city, road, and protected area and water sources)
were considered limitation and were deducted from the final map. Each criterion was fuzzified by IDRISI software and was put in the
numerical range 0-255. Afterwards, all standardized layers were multiplied by each weight of network analysis model by combining
ANP and Fuzzy models.
Eventually, overlapping layers was conducted by OWA model. OWA model has great flexibility to fulfill decision-makers' needs and
prioritization by providing various results with different levels of risk and compensation.
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Solar power plant site selection in Yazd province
Identifying of criteria and constraints
Constraints
Protected area
Criteria
CLIMATIC
GEOMORPHOLOGICAL
Road
Humidity
Slope
Urban area
Solar
radiation
Aspect
water resource
LOCATION
ENVIRENMENTAL
Land cover
cover
Distance
from fault
Elevation
Distance
from urban
Distance
from road
Geology
GIS Database
Weighting of criteria based on ANP method
Making fuzzy criteria maps
Overlaying the layers with OWA model
Reclassification of final map
Analyzing final land suitability map
Figure2 . Conceptual model for solar power plant site selection
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2.2.1. Analytic Network Process(ANP model)
Analytic Network Process is one of the multiple criteria decision making techniques and fall into the category of compensation models.
It is a useful tool because it allows modelling the problem as a network of criteria and alternatives (which are called elements) and it
is arranged in clusters [33]. Since analytical network process is a general and advanced mode of AHP, it contains AHP’s positive
features such as simplicity, flexibility, simultaneous usage of qualitative and quantitative criteria, and the ability to check the
compatibility of judgments. In addition, it can consider the complex relationships (interdependencies and feedbacks) between and
among the decision elements via applying the network structure rather than a hierarchical structure. All elements in a network can be
associated with each other in any way. In other words, feedbacks and interactions between and among the clusters are possible in a
network. The mentioned feature of ANP makes it possible to consider the interdependencies between the elements thus provides
precise attitude to the issues [34].
The ANP consists of the following major steps:
Step 1. Structuring the problem like a network. The problem should be stated clearly. It should be classified like clusters including
the alternative cluster which is made up of various elements or nodes. Then, the relationships between nodes and clusters should be
identified (Figure 3).Thus, it models a network with cycles connecting its nodes and loops that connect components to themselves.
Decision makers can obtain the network structure through brainstorming or other appropriate methods [35].The whole complex of
interrelationships should be represented through a matrix of inter factorial domination [36].
Figure3. designing conceptual model by ANP model
Step 2. Criteria are compared, using Super Decisions software (Figure 4), in the whole network in order to form an un weighted super
matrix by pairwise comparisons conducting a pairwise comparison on the elements to establish the relative importance of the different
elements. With respect to a certain component of the network, experts and stakeholders are individually asked to respond to a series
of pairwise comparisons. The comparisons are made using Saaty's Fundamental Scale which is a 9-point scale where a score of one
represents equal importance between the two elements and a score of 9 indicates the great importance of one element compared to the
other one [37].
Since the comparisons are carried out through personal judgment, it is vital to verify their consistency in order to guarantee that we
can depend on the judgments. Saaty introduced the consistency index (CI) and the consistency ratio (CR) as follows:
CI=θmax-n/n-1
(1)
CR=CI/RI
(2)
Where CI is the consistency index; θmax is the maximum eigenvalue; n is the number of elements in the judgment matrix; CR is the
consistency ratio and; RI is the consistency index of a randomly generated reciprocal matrix from the 9-point scale, with forced
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reciprocals. The CR is a measure of how a given matrix compares to a purely random matrix in terms of the CI [38]. For matrixes
larger than 3 × 3, a value of the CR ≤ 0.1 is considered acceptable while the decision-maker should revise their judgments for larger
values of the CR. Pairwise comparisons between the applicable elements are used as the input to calculate the weighted priority vector
which is related to the main eigenvector of the comparison matrix.
Figure4 . Pairwise comparisons in supper decisions software
Step 3. Forming a super matrix to obtain global priorities in a system with interdependent influences. The local priority vectors are
entered in the suitable columns of a matrix - known as a super matrix. It is a partitioned matrix where each sub-matrix consists of a
set of relationships between and within the levels. In the first step, unweight super matrix is obtained which contains all the
eigenvectors derived from the pairwise comparison matrices of the model. Subsequently, to obtain the weighted super matrix, the
eigenvector obtained from a cluster level comparison with respect to the control criterion is applied to unweight super matrix as a
cluster weight. If there is interdependence among clusters in a network, as generally happens, the columns of the super matrix sum to
More than one. Therefore, the super matrix must be changed to make it stochastic. [33].
Step 4. Final priorities
Eventually, the weighted super matrix is converged to obtain a long-term stable set of weights. In this process, the weighted super
matrix is increased to the power of 2k, where k is an arbitrarily large number. The outcome of such operation is called the limit super
matrix [33]. The final priorities of all the elements in the matrix can be obtained by normalizing each block of the limited super matrix.
Since the weighted super matrix usually covers the whole network, the priority weights of alternatives can be found in the column of
alternatives in the normalized super matrix. The alternative with the largest overall priority would finally be selected because it is the
best alternative as determined by the related calculations which are made by using matrix operations [35].
2.2.2. Fuzzy modeling approach
For the first time, Professor Askar Lotfi Zadeh developed Fuzzy logic under uncertainty conditions. This theory is capable of
formulating many inaccurate and unambiguous variables and systems and concepts and provide control and decision making under
uncertainty. The ability of GIS systems in Raster map analysis, makes it possible to implement different techniques such as Fuzzy
because the positive and negative threshold data (0 to 1, not binary) the degree of membership of the variables would be determined.
The fuzzy logic approach creates more flexible compositions of weighted maps and it can be easily implemented with GIS modeling
language [39]. Values are selected based on subjective judgment to show the membership degree of the set (Fig. 5).Parameters of
positioning problems largely have Fuzzy nature. For example, factors relating to proper distance from some complications are Fuzzy
collections. Each pixel according to its distance from complication has a different membership degree in this collection. Pixels
membership criterion in the collection is being appropriate or inappropriate and is determined between 0 and 1. These values are
established using the knowledge of experts. One of the most important steps in fuzzy logic is defining Fuzzy membership value for
each criterion. In this model, the membership of an element in the collection would be defined in a range of 1 (full membership) to
zero (non-membership) [40]. For this reason, the Membership Fuzzy operating instruction is used. Actually, the definition of the
Fuzzy membership (or standardizing the criteria) is one of the main steps of Multiple Criteria Decision Making (MCDM).
Fuzzy membership functions can be classified from two aspects: Type and Shape. Types include an S-shaped (Sigmoidal), J-shaped
(Jshaped), and linear. Shapes include monotonically increasing, monotonically decreasing, and symmetric [41]. Weights and control
point of criteria are presented in Table 1, based on expert opinions and reviewing scientific articles and technical reports. Figure 6
shows digital layers of Fuzzy membership.
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Authors /Journal of Environmentally Friendly Processes 3 (2014) 45-47
Figure5. Fuzzy membership function diagram [42].
Table 1. The final control points and weights for suitability assessment of the criteria land for Yazd province
Fuzzy function
b
c
reasons of
selection
Chart type
d
Potential
solar
radiation
7*105
(wh/ m2
/y)
1*106
(wh/ m2
/y)
-
-
0.278
Increase the
intake of
energy from
the solar [43]
Sigmoidal
/Monotonically
increasing
Humidity
25℅
-
-
40℅
0.139
Absorbing
solar energy by
carbon dioxide
and water
vapor and
reducing
energy intake
[44].
Sigmoidal Monotonically
decreasing
Elevation
500
1500
2000
3500
0.052
Dependence on
temperature ,
rainfall and
solar potential
[45]
Linear/Symmetric
slope
3℅
10℅
20℅
100℅
0.105
Reduce costs
and
construction of
bigger plants
[44].
Linear/Symmetric
CLIMATIC
a
Weight
GEOMORPHOLOGICAL
criteria
7
N,NE
(0–45)
S,F
(0,180)
SW,
SE,W
(225,135)
E, NE
(90,45)
0.037
Increase the
intake of
energy from
the solar and
reduce the cost
of power
Sigmoidal/Symmetr
ic
Geology
hard
usual
-
-
0.074
Reduce costs
and
construction of
bigger plants
[44].
Sigmoidal
/Monotonically
increasing
Distance
from urban
2000m
5000m
20000m
80000m
0.056
Reducing the
cost of transfer
and waste of
energy [46].
Sigmoidal/Symmetr
ic
Distance
from roads
1000 m
5000m
15000m
80000m
0.063
Accessibility to
the site and
reduce costs
for facilities
[43].
Sigmoidal/Symmetr
ic
Distance
from faults
2000
-
0.063
Security and
reduction of
cost of
reconstruction
and repair
Linear/Monotonical
ly increasing
land cover
25-50℅
>50℅
0.66
Protection of
natural
environment
Linear/Symmetric
Constraint
Parameter
4000
Main road
-
no range
<25℅
Protecte
d area
Water
resource
Urban
area
1000m
250m
500 m
ENVIRENMENTAL
Aspect
LOCATION
Authors /Journal of Environmentally Friendly Processes 3 (2014) 45-47
250 m
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Figure 6.a) Aspect b) Elevation c) Slope s d) Fault e) Geology f) Humidity g) Land cover h) Potential solar
radiation i) City j) Road
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Authors /Journal of Environmentally Friendly Processes 3 (2014) 45-47
2.2.3. Extracting the restrictive
Based on a series of criteria a particular inappropriate occasion for a given user would be determined. Restrictions are preventing
rules imposed by man or nature; they make the options inapplicable. We investigated the region, resources, and standards. Then we
regarded the following layers as restricted areas and removed them from the final map: cities, roads, water resource and protected
areas. Figure 7 shows the restriction maps.
Figure7. a) Protected area b) Road c) Urban area d) water resource e) Final exclusionary map for solar plants
site selection in Yazd Province
2.2.4. Ordered weighted averaging (OWA model)
Ordered weighted averaging (OWA) technique is one of the combining methods in MCDM methods and has been developed based
on the theory of Fuzzy sets [11]. Using this method is not limited to fuzzy sets [47]. Also a new concept for the development of
Boolean (classic) decision rules and weighted linear combination has been provided. This new concept includes ordinal weights that
are different from standard weights. Standard weights have been assigned to the using criteria while the ordinal weights are assigned
to the values of criteria pixel by pixel. In spatial decision-making, determining and applying an appropriate set of ordinal weights we
can result a wide range of outcomes (maps). In other words, this method is highly flexible in meeting the needs and priorities of
decision makers because it can provide different results with different levels of risk and compensation (balance) (Figure 8). The OWA
technique is comprised of three kinds of composite functions including the Union (OR), the Intersection (AND), and the Fuzzy
average. The operator plays a balancing role in the category of Fuzzy operators and creates a good condition regarding to the related
degree of AND and OR. This method allows the decision-maker to control decision-making on both the Risk and Balance axes. The
OWA operator includes the Fuzzy Minimum and Maximum. The Maximum operator matches the logical OR and the Minimum
operator matches the logical AND. Choosing the most fitted location in decision making implies a low level of risk. In this case, we
should use the Boolean method or the similar method AND. If the purpose of decision-making is the minimum requirements, we
should use the OR operator, which indicates a high risk in decision-making. In OWA method, we can simultaneously change the risk
and balance levels of the measures so that the Risk level can vary from the minimum to maximum and the Balance level vary from
no balance mode to the Maximum balance mode and then to the no balance mode [48].
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Figure8 . Decision Strategy Space in the Ordered Weighted Average [41]
OWA operator is defined as follows:
𝑂𝑊𝐴𝑖 =∑𝑛𝑗=1(𝑢𝑗 𝑣𝑗 /∑𝑛𝑗=1 𝑤𝑗 𝑣𝑗 )𝑧𝑖𝑗
Where z_i1≤ ... ≤ zin result from putting the values of one criterion in order. Vi is the ordinal weight and Wj is the standard ordered
weight. OWA operator consists of two main characteristics that represent the behavior and position of the operator. 1-orness degree
2- permutation balance relationship. Orness degree represents the OWA operator position in relations between and (minimum) and
Or (maximum) and represents the risk-taking and risk aversion of the decision maker. The Orness degree is defined as follows:
𝑛
∑𝑛𝑖=1(𝑛 − 𝑖). 𝑣𝑖
Orness=
𝑛−1
The second feature of OWA operator is showing the degree one index can be exchanged or affected by the other indices, expressed
as follows:
Trade off= 1- √
𝑛
𝑛−1
1
∑𝑛𝑖=1(𝑣𝑖 − )2
𝑛
Where Vi is the criteria ordinal weight and n is the number of the criteria [49].
The final classified map obtained from fuzzy models, ANP and OWA in combination with the county maps of the province is
shown in Figure 9 and Table 2 shown suitability amount of classes.
3. Result
In this study, important criteria were detected in order to select a suitable location to establish a solar power plant in the study area
and were weighted by the process of weighting analytical network. The limitations for locating a power plant were recognized and
were omitted from the final map (Figure9). The proposed limitations were the ones which ought to be protected in location. Having
fuzzified and having combined layers and their weights, the layers were put over one another by OWA.
In order to acquire the highest value to establish a solar power plant, the final map was divided into 10 categories(Figure 10). Five
sites were introduced the candidate sites. The results showed that application of fuzzy logic, OWA, MCDM and ANP model have a
high accuracy in locating solar power plant. In the study area, a lot of regions can be determined to be high suitability. Sites 1 and 2
are located in Ardakan County and Sites 3, 4 and 5 are in Bafq County. Table 3 shows the characteristics of five sites. These sites
reduce the power plant investment costs due to locating in a suitable place.
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Figure9 . The final solar power plant site selection map
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Figure.10 a. Meybod County b. Abarkuh County c. Ardakan County d.Bafgh County e. Khatam County f.
Mehriz County g. Ashkezar County h. Taft County i. Yazd County
Table 2 The suitability amount of classes
Khatam County
Classess
Area(℅)
1
2
3
4
8
15
65
12
Description of suitability
Unsuitable
relativity unsuitable
moderately suitable
suitable
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Authors /Journal of Environmentally Friendly Processes 3 (2014) 45-47
Abarkuh County
1
2
3
4
1
51
40
8
Unsuitable
relativity unsuitable
moderately suitable
suitable
Mehriz County
1
2
3
4
36
33.5
23
7.5
Unsuitable
relativity unsuitable
moderately suitable
suitable
Taft County
1
2
3
4
1
37
51
11
Unsuitable
relativity unsuitable
moderately suitable
suitable
Yazd County
1
2
3
4
14
42
23
21
Unsuitable
relativity unsuitable
moderately suitable
suitable
Ashkezar County
1
2
3
4
3
79
14
4
Unsuitable
relativity unsuitable
moderately suitable
suitable
Meybod County
1
2
3
4
19
15
61
5
Unsuitable
relativity unsuitable
moderately suitable
suitable
Bafgh County
1
2
3
4
6
45
25
23
Unsuitable
relativity unsuitable
moderately suitable
suitable
Ardakan County
1
2
3
4
13
57
18
12
Unsuitable
relativity unsuitable
moderately suitable
suitable
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Figure 11. Candidate sites for solar power plant
Candid
ate
sites
slope
Distance
from fault
(m)
elevation
Solar radiation
(wh/ m2 /y)
Land cover
humidity
(m)
Distance
from urban
(m)
Distance
from
road
(m)
1
15℅- 17℅
>4000
1750
780642-826177
no range
23℅
<4000
<5500
2
18℅-20℅
>3500
1800
832523-87456
no range
22℅
<6000
<3500
3
16℅-18℅
>3000
1850
806899-837808
no range
24℅
<2000
<5000
4
19℅-21℅
>5000
1900
86486-92456
<25℅
24-℅
<6500
<4000
5
20℅-22℅
>8000
2000
851012-97456
<25℅
25℅
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4. Conclution
With regard to the economic development and population growth, the domestic fossil fuels demand is steeply rising. It is necessary
to consider the use of renewable energies such as solar energy in order to meet part of the electrical and thermal energy requires,
meeting the goals of sustainable development, diversification of the domestic energy mix, and reducing the consumption of fossil
fuels. After determining effective factors and their role in locating solar power plant in this study, we provided the relevant layers of
information and required analyses. Finally, integrating different information layers and applying the restrictions, we found the suitable
area to construct the solar power plant. Decision-making about the appropriate location for power plant construction requires
considering several factors simultaneously. The Geographic Information System provides a consistent possibility to integrate
information layers relating to the mentioned layers. Selected locations are entirely affected by the parameters included in the analysis
and the related weights. The ANP model is a suitable model for weighting and valuating layers in the GIS environment. It provides
suitable sites for power plant construction. In this research, we made the maps Fuzzy in IDRISI environment. Using Analytical
Network Process (ANP) we conducted weighting by Super decision software. Finally, we overlaid the maps using ordered weighted
averaging (OWA) method. We resulted the related zones by using the GIS environment. We found the final map, which indicates the
areas with a good potential for exploitation of solar energy. Comparing to the recent research in the field of locating power plants, the
Fuzzy and Analytical Network Analysis (ANP) process and OWA in GIS environment are the top priority and able to take the
qualitative and quantitative objectives into account (with the proper utilization of the experts’ comments) and result the optimal mix
of production. The results of analyzing the counties of Yazd province in terms of establishing solar power plants (Fig 10) showed that
from among 10 counties of the province, Bafgh County, with the highest percentage in the class, has been chosen as the best county
for the construction of solar power plant. According to the final map five sites were introduced for candidate sites in order to stablishing
solar power plant site selection.
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